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That's probably true in terms of benchmark problems, but at the same time, losing knowledge of seemingly irrelevant things could make the specialised model less
by fauigerzigerk 3y ago
That's probably true in terms of benchmark problems, but at the same time, losing knowledge of seemingly irrelevant things could make the specialised model less creative when it comes to looking for unexpected causes and links.
It would also be less robust in the face of exceptional situations that should make it doubt the reliability and relevance of its own training.
For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians".
A specialised diagnostic system might not notice signs of domestic violence that a GP would see. Being able to connect the dots beyond the confines of some specialised field is extremely useful in many situations.
- notahacker 3y ago> For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I think that argument works better the other way. I'm outspoken in arguing many of the edge cases in driving are general intelligence problems we're not about to solve by optimising software over a few billion more miles in a simulator, but I don't want that intelligence so generalised that my car starts running down elderly pedestrians because there's been a lot of zombie literature on the Internet lately. I'm pretty confident there's more risk of people dying from that than a zombie apocalypse.
- skinner_ 3y ago> For instance, an autonomous driving system that doesn't know the first thing about the zombie apocalypse could put its passengers' lives at risk by refusing to run over "pedestrians". I'd prefer my self-driving car not to learn about the zombie apocalypse, lest it starts running over pedestrians.